July 06, 2018

May 11, 2018

October 12, 2018

Every semester, I take a little poll in class, and the results are virtually the same. I tell my students to close their eyes and visualize a scientist working in a lab. (Go ahead, try it.) When they open their eyes, I ask them how many saw a woman.

Inevitably, the results are overwhelming. They almost always picture a male. In my most recent class of 60 students, for example, only two saw a woman. The exercise is invariably followed by sheepish glances around the room as they realize the extent of the stereotype.

As I am quick to explain to them, the fact that they jumped to the conclusion that the scientist would be a male doesn’t mean they are sexist; it simply means that some of them might have just experienced implicit bias.

Implicit bias is a hot-button topic today. People often conflate it with outright bigotry, but the two are not the same. The truth is that our brains are wired to make generalizations—mental shortcuts that help us filter and make sense of all the information we are presented with in a very large world.

Looking at it from another, perhaps less emotionally charged direction, how many times have you met someone and were struck by his or her unfriendliness, only to learn later that the person was painfully shy or perhaps was having a very bad day? Again, that’s what our brains do. They make judgment shortcuts. And clearly, those shortcuts can be wrong.

Why do these implicit biases matter to you in the workplace? Because they can subconsciously affect your decisions: decisions such as whom to hire or promote. A famous example is the move in the 1970s and 1980s by symphony orchestras to begin conducting blind auditions, in which candidates auditioned from behind a screen.

Although the composition of the largest, most prestigious orchestras at the time was about 95 percent male, the initial purpose of the change was not to address gender disparity; it was to eliminate the favoritism that was thought to be shown to students of certain prestigious teachers.

What orchestras found, though, is that blind auditions greatly increased the selection of women. By the early 1990s, the proportion of women in orchestras had increased to more than 20 percent, and it continued to rise after that. (Interestingly, in some cases, the percentages weren’t affected until judges started asking women to remove their shoes, thereby eliminating the telltale click of high heels.)

Another example of implicit bias was demonstrated through a study published in Administrative Science Quarterly in September 2016. The authors of the study created two versions of resumes for black and Asian applicants—one that contained information indicating their minority status in some way and another that did not give any perceived clues to racial identity. The applicants then submitted these resumes for more than 1,600 entry-level positions on various job sites.

Despite the fact that the resumes were identical in qualifications, the differences in responses from companies were significant. While 25 percent of the resumes for blacks with no clues to racial identity received responses, only 10 percent of those where race was clearly identifiable received callbacks. The numbers for Asians were similar: 21 percent vs. 11.5 percent.

Is it possible that some of the people screening resumes were overtly prejudiced? Of course. But what is much more likely at play here is implicit bias. The recruiters’ brains were making subconscious judgment shortcuts. The individuals screening the resumes would likely be surprised to learn they had inadvertently screened out many more minorities, but as much as we try to make the hiring process impartial, human tendencies often rule the day.

Some people think the advent of artificial intelligence in the recruitment process will eliminate these human biases and that computers will focus solely on qualifications. We have a very long way to go before that happens, though, as Amazon recently determined. The company’s computer experts had been working for several years on a program that would rate candidates and eliminate human error, but they dropped the project when they realized the program discriminated against female applicants. The problem? Algorithms are based on past data—in Amazon’s case, the last 10 years of applications. Since most of those applications came from men, the computer taught itself to discriminate against women.

The situation isn’t hopeless, by any means. Computers might eventually get it right. But until then, implicit bias doesn’t have to be inevitable in the process of hiring or promoting employees. Simply acknowledging to yourself that the potential for unconscious bias is always there is a great start, and consciously choosing to try to avoid it is the next step.•

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Malatestinic is a senior lecturer in human resource management at the Indiana University Kelley School of Business at IUPUI.

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